ERNIE 4.5 21B A3B Thinking
ERNIE 4.5 21B A3B Thinking is Baidu’s upgraded lightweight MoE language model optimized for deep reasoning, with a context window around 131K tokens and competitive pricing for large-scale use.
What is ERNIE 4.5 21B A3B Thinking?
ERNIE 4.5 21B A3B Thinking is a 21B-parameter sparse Mixture-of-Experts language model from Baidu, designed to activate about 3B parameters per token for efficient high-quality reasoning. It is mainly used for complex multi-step logical reasoning, math and science problem solving, and expert-level academic or benchmark tasks. It is also applied to coding assistance and advanced text generation where long-context (≈131K tokens) understanding is required at relatively low cost per token. The model belongs to Baidu’s ERNIE 4.5 family as a reasoning-enhanced successor to earlier ERNIE 4.x and ERNIE 3.x variants.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| Baidu | ~$0.80 | ~$2.40 | — | ~280ms | ~40 tps | 99.9% |
| Alibaba Cloud | ~$0.90 | ~$2.70 | — | ~260ms | ~35 tps | 99.9% |
| Tencent Cloud | ~$0.95 | ~$2.85 | — | ~300ms | ~30 tps | 99.9% |
Try this model
Test ERNIE 4.5 21B A3B Thinking right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://inference.example.com/v1"
)
response = client.chat.completions.create(
model="baidu/ernie-4-5-21b-a3b-thinking",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "baidu/ernie-4-5-21b-a3b-thinking",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Advanced Reasoning
Performs complex multi-step reasoning for logical puzzles, math, science, and academic-style problems using an MoE thinking architecture.
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Chat Completion
Acts as a conversational chat model, generating coherent, context-aware responses for interactive dialogue and assistant-style applications.
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Text Generation
Produces long-form, structured written content and explanations over very long contexts up to around 128K–131K tokens.
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Multilingual Support
Understands and generates text in both Chinese and English, suitable for bilingual tasks and cross-language information access.
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Tool-Assisted Tasks
Provides efficient tool usage capabilities, supporting structured interactions like function or tool calling in complex workflows.
6 Most Valuable Use Cases
- Complex Logical Reasoning
- Mathematics Problem Solving
- Scientific Text Generation
- Advanced Code Assistance
- Academic Benchmark Tasks
- Structured Tool-Based Workflows
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent Model Routing
Automatically route each request to the optimal model across providers using policies, latency, and quality signals—no client changes or new integrations required.
One endpoint, every model -
Cost-Aware Optimization
Enforce budgets, pick cheaper equivalents, and downgrade gracefully under load so you control spend without touching application code or sacrificing SLAs.
Lower costs, same output -
Resilient Fallback Flows
Define provider and model failover chains so requests auto-retry on alternates, shielding your app from outages, rate limits, and sudden model deprecations.
Stay online, automatically -
End-to-End Observability
Trace every call across providers with logs, metrics, and structured events to debug failures, compare models, and tune prompts from one unified view.
See every token hop -
Task-Level Orchestration
Declare tasks, tools, and constraints once; LLM.API handles planning, multi-step execution, and provider selection for robust, reusable AI workflows.
From prompts to workflows -
High-Throughput Batch Runs
Ship millions of inferences as managed batches with automatic chunking, retries, and aggregation so you can backfill datasets or experiments at scale.
Crank up the throughput
When to Use — When NOT to Use
Use it if...
- You need a Chinese-centric LLM optimized for mainland China language and content ecosystems.
- You need strong performance on Chinese reading comprehension, classification, and knowledge-intensive tasks.
- Your use case involves Chinese dialogue agents integrated with Baidu search-style knowledge retrieval.
- Your use case involves Chinese enterprise applications already deployed on Baidu Cloud infrastructure.
- You need large-scale Chinese content generation, summarization, or rewriting for consumer-facing products.
- You need alignment with Chinese regulatory requirements and content governance out of the box.
Avoid if...
- You need top-tier English reasoning performance competitive with the latest frontier global models.
- Your workload requires extensive support for niche non-Chinese languages and low-resource locales.
- You need fully transparent licensing, benchmarking, and community tooling typical of open Western ecosystems.
- Your workload requires tight integration with US- or EU-centric cloud, MLOps, and governance stacks.
- You need proven performance on cutting-edge multimodal tasks beyond text, like advanced vision-language.
- Your workload requires detailed public documentation, SDKs, and examples for non-Chinese-speaking developers.
Frequently Asked Questions
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What is ERNIE 4.5 21B A3B Thinking?
ERNIE 4.5 21B A3B Thinking is a 21-billion-parameter Baidu large language model focused on reasoning-heavy text generation tasks.
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What is ERNIE 4.5 21B A3B Thinking best suited for?
It is best for multi-step reasoning, complex code understanding, tool-using agents, and analytical workflows where chain-of-thought quality matters more than raw speed.
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What is the context window of ERNIE 4.5 21B A3B Thinking via LLM.API?
ERNIE 4.5 21B A3B Thinking supports up to a 32K token context window on LLM.API, including prompt and generated tokens.
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How is ERNIE 4.5 21B A3B Thinking priced on LLM.API?
LLM.API charges per 1,000 input and output tokens for this model; check your LLM.API pricing page for current rates.
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How fast is ERNIE 4.5 21B A3B Thinking in terms of latency?
Typical first-token latency is a few hundred milliseconds to a couple of seconds, depending on load, with streamed tokens arriving progressively.
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Which modalities does ERNIE 4.5 21B A3B Thinking support on LLM.API?
On LLM.API, ERNIE 4.5 21B A3B Thinking currently supports text input and text output only.
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How do I call ERNIE 4.5 21B A3B Thinking through LLM.API?
Use the standard LLM.API chat or completion endpoint and set the model field to the ERNIE 4.5 21B A3B Thinking identifier.
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How does ERNIE 4.5 21B A3B Thinking compare to similar 20–30B models?
Compared with similar-sized models, it emphasizes stronger step-by-step reasoning but may be slower and more expensive per request.
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What are the main limitations of ERNIE 4.5 21B A3B Thinking?
It can hallucinate facts, has no real-time web access, and may struggle with highly specialized domain knowledge without careful prompting.
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Can I use ERNIE 4.5 21B A3B Thinking with tools and function-calling on LLM.API?
Yes, you can pair it with LLM.API's tool-calling mechanisms, but tool schemas and orchestration logic must be implemented on your side.
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